A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images
This addresses the need for robust nematode detection to monitor crop damage and support biological studies, representing an incremental improvement with a novel annotation method.
The paper tackles the problem of detecting nematodes in microscopic images by proposing a CNN framework that uses line annotations along the body, achieving 75.85% precision and 73.02% recall on a potato cyst nematode dataset and 84.20% precision and 85.63% recall on a public C. elegans dataset.
Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biological studies involving the nematode C. elegans, a common model organism. Here, we propose a framework for detecting worm-shaped objects in microscopic images that is based on convolutional neural networks (CNNs). We annotate nematodes with curved lines along the body, which is more suitable for worm-shaped objects than bounding boxes. The trained model predicts worm skeletons and body endpoints. The endpoints serve to untangle the skeletons from which segmentation masks are reconstructed by estimating the body width at each location along the skeleton. With light-weight backbone networks, we achieve 75.85 % precision, 73.02 % recall on a potato cyst nematode data set and 84.20 % precision, 85.63 % recall on a public C. elegans data set.